Spatiotemporal whole-brain activity and functional connectivity of melodies recognition

Abstract Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.


Figure SF1. Musical sequences used in the experiment.
Depiction of all musical sequences used in the auditory 'old/new' paradigm employed in the study.On the left, we have reported the 40 musical sequences extracted from Bach's prelude (previously memorised musical sequences, 'old').On the right, we have reported the 40 novel melodies (novel musical sequences, 'new') that were created and matched to Bach's prelude excerpts with regards to IC, H and main acoustic features.

SUPPLEMENTARY TABLES
For supplementary tables too large to be included in this document, please refer to the corresponding Excel files available in the following GitHub repository: https://github.com/leonardob92/MelodiesRecognition_LB2017_BroadbandActivity_StaticFunctionalConnectivity.git   comparing it with the original analysis pipeline.

Table ST5 -Brain activity for each tone of the musical excerpts
Significant clusters of brain activity associated with each tone of the musical excerpts, reported for both conditions (memorised and novel musical sequences) and for their contrast (memorised versus novel musical sequences).The table also shows the results for the alternative pre-processing pipeline, comparing it with the original analysis pipeline.

Figure SF2 .
Figure SF2.Brain activity underlying memorised versus novel musical sequences: comparison between preprocessing pipelines.The figure shows the results reported in the main text (labelled as 'Original analysis pipeline') versus the results obtained using a shortened pre-processing pipeline (labelled as 'Alternative preprocessing analysis pipeline) which consisted only of MaxFilter and ICA for removing eyeblink and heart-beat artefacts.The figure shows that the results are very similar.Decoding -Multivariate pattern analysis decoding the different neural activity associated with memorised versus novel musical sequences.Decoding time series (left, grey areas indicate significant time-points), spatial sequences depicted as topoplot (middle left), temporal generalisation decoding accuracy (middle right) and statistical output of significant prediction of training time on testing time (right).Cluster-based MCS -The left plot shows the amplitude associated with memorised (red) and novel musical sequences (blue).Grey areas show the time-points where the difference between memorised and novel sequences was significant.The middle plot illustrates a couple of topoplots showing brain activity for gradiometers (left, fT/cm) and magnetometers (right, fT) within the significant time-window emerged from the MCS.The values represent the statistics (t-values) contrasting the brain activity underlying recognition of

Figure SF3 .
Figure SF3.Brain activity over time: comparison between pre-processing pipelines.The figure shows the results reported in the main text (labelled as 'Original analysis pipeline') versus the results obtained using a shortened pre-processing pipeline (labelled as 'Alternative pre-processing analysis pipeline) which consisted only of MaxFilter and ICA for removing eyeblink and heart-beat artefacts.The figure shows that the results are very similar.The top row depicts an example trial for the memorised sequences.Red tones illustrate the dynamics of the musical excerpt.The second row (represented independently for the two pre-processing pipelines) indicates the brain activity (t-values) associated with the recognition of previously memorised versus novel musical sequences.

Figure SF4 .
Figure SF4.Confusion matrix over timeConfusion matrix of the multivariate pattern analysis (decoding) computed for each time-point.The four time series show the decoding accuracy for the four following combinations: predicted memorised sequences -actual memorised sequences (i); predicted novel sequences -actual memorised sequences (ii); predicted memorised sequences -actual novel sequences (iii); predicted novel sequences -actual novel sequences (iv).The plot shows that the best decoding accuracy was for predicted novel sequences -actual novel sequences.

Figure SF5 .
Figure SF5.Brain activity underlying auditory sequence recognition and musical skills.The first plot from the left shows the contrast between brain activity underlying recognition of the previously memorised versus novel musical sequences.The second plot illustrates such contrast in relation to the musicianship groups of our sample (pianists, non-pianist musicians, non-musicians).The remaining plots show the correlation between the contrast and three measures of musical skills/features: aesthetical appreciation of the Bach's prelude (i), familiarity with the Bach's prelude (ii), general ability to be engaged with music and musical activities (iii).

Figure SF6 .
Figure SF6.Static functional connectivity for 2-8Hz.a -Matrix representation of contrasts (t-values) between task versus baseline SFCs.b -Same values depicted in schemaballs.c -Same values depicted in brain templates.For each pair of brain templates, the left brain represents a left hemisphere perspective while the right one a posterior view of the brain.

Table ST1 .
Significant clusters of MEG sensors emerged from MCS contrasting memorised versus novel musical sequences.The table depicts these clusters independently for gradiometers and positive and negative magnetometers.

Table ST2 .
Significant clusters of MEG sensors emerged from MCS contrasting novel versus memorised musical sequences.The table depicts these clusters for gradiometers.

Table ST3 -Detailed information on significant clusters for MEG sensor data
Significant clusters of MEG sensors emerged from MCS contrasting memorised versus novel musical sequences.The table depicts those clusters with regards to significant channels and time-windows.The table also shows the results for the alternative pre-processing pipeline, comparing it with the original analysis pipeline.

Table ST4 -Detailed information on significant clusters for MEG source data
Significant clusters of MEG sources emerged from cluster-based permutation testing and related to memorised musical sequences versus baseline, novel musical sequences versus baseline and memorised versus novel musical sequences.The table depicts those clusters with regards to significant voxels, time-windows, and averaged t-values for each voxel.The table also shows the results for the alternative pre-processing pipeline,